mirror of
https://github.com/storytold/torchscript-models.git
synced 2026-10-18 07:33:20 +00:00
Brandon Thomas
105 lines
3.0 KiB
Python
105 lines
3.0 KiB
Python
""" from https://github.com/keithito/tacotron """
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import re
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from text import cleaners
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from text.symbols import symbols
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# Mappings from symbol to numeric ID and vice versa:
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_symbol_to_id = {s: i for i, s in enumerate(symbols)}
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_id_to_symbol = {i: s for i, s in enumerate(symbols)}
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print("Symbol to ID table:")
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for k, v in _symbol_to_id.items():
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print("\"{}\" => {},".format(k, v))
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# Regular expression matching text enclosed in curly braces:
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_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
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def get_arpabet(word, dictionary):
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word_arpabet = dictionary.lookup(word)
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if word_arpabet is not None:
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return "{" + word_arpabet[0] + "}"
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else:
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return word
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def text_to_sequence(text, cleaner_names, dictionary=None):
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'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
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The text can optionally have ARPAbet sequences enclosed in curly braces embedded
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in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
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Args:
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text: string to convert to a sequence
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cleaner_names: names of the cleaner functions to run the text through
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dictionary: arpabet class with arpabet dictionary
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Returns:
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List of integers corresponding to the symbols in the text
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'''
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sequence = []
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space = _symbols_to_sequence(' ')
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# Check for curly braces and treat their contents as ARPAbet:
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while len(text):
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m = _curly_re.match(text)
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if not m:
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clean_text = _clean_text(text, cleaner_names)
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if dictionary is not None:
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clean_text = [get_arpabet(w, dictionary) for w in clean_text.split(" ")]
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for i in range(len(clean_text)):
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t = clean_text[i]
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if t.startswith("{"):
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sequence += _arpabet_to_sequence(t[1:-1])
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else:
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sequence += _symbols_to_sequence(t)
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sequence += space
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else:
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sequence += _symbols_to_sequence(clean_text)
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break
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sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
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sequence += _arpabet_to_sequence(m.group(2)) # "HH AW1 S" -> ?
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text = m.group(3)
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# remove trailing space
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if dictionary is not None:
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sequence = sequence[:-1] if sequence[-1] == space[0] else sequence
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return sequence
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def sequence_to_text(sequence):
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'''Converts a sequence of IDs back to a string'''
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result = ''
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for symbol_id in sequence:
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if symbol_id in _id_to_symbol:
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s = _id_to_symbol[symbol_id]
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# Enclose ARPAbet back in curly braces:
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if len(s) > 1 and s[0] == '@':
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s = '{%s}' % s[1:]
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result += s
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return result.replace('}{', ' ')
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def _clean_text(text, cleaner_names):
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for name in cleaner_names:
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cleaner = getattr(cleaners, name)
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if not cleaner:
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raise Exception('Unknown cleaner: %s' % name)
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text = cleaner(text)
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return text
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def _symbols_to_sequence(symbols):
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return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
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def _arpabet_to_sequence(text):
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return _symbols_to_sequence(['@' + s for s in text.split()])
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def _should_keep_symbol(s):
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return s in _symbol_to_id and s is not '_' and s is not '~'
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